To install this package, start R (>= 3.5.0) and enter:
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
if (!require("methPLIER", quietly = TRUE)) install.packages("methPLIER")library(methPLIER)data(gse39279.data, package = "methPLIER")
data(gse39279.annot, package = "methPLIER")
data(methPLIER, package = "methPLIER")gse39279.data[1:5, 1:5]
#> GSM959318 GSM959328 GSM959330 GSM959332 GSM959334
#> cg00000029 0.5039425 0.3595960 0.1498145 0.2554182 0.2105006
#> cg00000108 0.8833132 0.9030418 0.9173808 0.9079961 0.9045386
#> cg00000109 0.7518248 0.8068435 0.8288920 0.8419195 0.7703245
#> cg00000165 0.4170720 0.2697226 0.5577269 0.3037358 0.7619721
#> cg00000236 0.7836071 0.7821850 0.8232099 0.8192344 0.8179069
gse39279.annot %>%
head()
#> # A tibble: 6 × 14
#> AccessionNo SampleName Category CUREP_Code Stage Smoking Age Sex TNM
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 GSM959318 Tumor 10 adenocarcin… CUREP1-127 I yes 49 male T1N0…
#> 2 GSM959328 Tumor 50 adenocarcin… CUREP1-297 I yes 62 fema… T1N0…
#> 3 GSM959330 Tumor 54 adenocarcin… CUREP1-301 I yes 68 fema… T2N0…
#> 4 GSM959332 Tumor 49 adenocarcin… CUREP1-296 I yes 57 fema… T1N0…
#> 5 GSM959334 Tumor 53 adenocarcin… CUREP1-300 I no 61 fema… T2N0…
#> 6 GSM959336 Tumor 47 adenocarcin… CUREP1-293 I yes 63 fema… T1N0…
#> # … with 5 more variables: TimeRec <dbl>, TNM_6th <chr>, TNM_7th <chr>,
#> # TumorSize <dbl>, Recurrence <chr>D <- getDmatrix(gse39279.data)D[1:3, 1:5]
#> GSM959318 GSM959328 GSM959330 GSM959332 GSM959334
#> A1BG 0.6494772 0.6463945 0.5993907 0.6373738 0.6335066
#> A2LD1 0.3252989 0.3416055 0.3733506 0.3348877 0.4208066
#> ABCD4 0.4156986 0.4147418 0.4158108 0.4321149 0.4175707B <- getNewDataB(D, methPLIER)B[1:3, 1:5]
#> GSM959318 GSM959328 GSM959330
#> 1,REACTOME_METABOLISM_OF_PROTEINS -0.8490864 -0.03498642 0.52888920
#> 2,REACTOME_METABOLISM_OF_PROTEINS 3.3874754 -0.27206180 -0.50793195
#> 3,REACTOME_METABOLISM_OF_PROTEINS -1.5019111 0.18072186 0.09608475
#> GSM959332 GSM959334
#> 1,REACTOME_METABOLISM_OF_PROTEINS -0.0009594372 0.22933095
#> 2,REACTOME_METABOLISM_OF_PROTEINS 0.9829585704 -0.61498552
#> 3,REACTOME_METABOLISM_OF_PROTEINS -0.6480075817 0.02028482ph <- plotHeatmap(B, k = 2, k.lv = 2)ph$cluster.sample
#> # A tibble: 155 × 3
#> cluster.sample rowid.sample AccessionNo
#> <int> <int> <chr>
#> 1 1 1 GSM959318
#> 2 2 2 GSM959328
#> 3 2 3 GSM959330
#> 4 1 4 GSM959332
#> 5 2 5 GSM959334
#> 6 2 6 GSM959336
#> 7 1 7 GSM959339
#> 8 2 8 GSM959344
#> 9 2 9 GSM959345
#> 10 2 10 GSM959346
#> # … with 145 more rowsph$cluster.LV
#> # A tibble: 524 × 3
#> LV `1` `2`
#> <int> <list> <list>
#> 1 1 <dbl [20]> <dbl [135]>
#> 2 2 <dbl [20]> <dbl [135]>
#> 3 3 <dbl [20]> <dbl [135]>
#> 4 4 <dbl [20]> <dbl [135]>
#> 5 5 <dbl [20]> <dbl [135]>
#> 6 6 <dbl [20]> <dbl [135]>
#> 7 7 <dbl [20]> <dbl [135]>
#> 8 8 <dbl [20]> <dbl [135]>
#> 9 9 <dbl [20]> <dbl [135]>
#> 10 10 <dbl [20]> <dbl [135]>
#> # … with 514 more rowscl <- ph$cluster.sample %>%
inner_join(gse39279.annot) %>%
mutate(cluster = paste0("cluster_", cluster.sample), Event = 1) %>%
dplyr::rename(Time = TimeRec) %>%
dplyr::select(cluster, AccessionNo, Time, Event)
plotSurvival(cl)topLV <- getTopLVs(ph$cluster.LV, "1", "2")topLV %>%
arrange(q.value)
#> # A tibble: 357 × 5
#> LV `1` `2` p.value q.value
#> <int> <list> <list> <dbl> <dbl>
#> 1 386 <dbl [20]> <dbl [135]> 2.79e-10 0.0000000730
#> 2 524 <dbl [20]> <dbl [135]> 1.81e-10 0.0000000730
#> 3 60 <dbl [20]> <dbl [135]> 6.08e-10 0.0000000848
#> 4 75 <dbl [20]> <dbl [135]> 8.09e-10 0.0000000848
#> 5 256 <dbl [20]> <dbl [135]> 6.59e-10 0.0000000848
#> 6 271 <dbl [20]> <dbl [135]> 9.72e-10 0.0000000849
#> 7 98 <dbl [20]> <dbl [135]> 1.80e- 9 0.000000118
#> 8 412 <dbl [20]> <dbl [135]> 1.75e- 9 0.000000118
#> 9 46 <dbl [20]> <dbl [135]> 3.02e- 9 0.000000131
#> 10 59 <dbl [20]> <dbl [135]> 3.30e- 9 0.000000131
#> # … with 347 more rowslibrary(patchwork)
topLV %>%
top_n(10, -q.value) %>%
pull(LV) %>%
map(~plotBoxplot(B, .x, ph$cluster.sample)) %>%
purrr::reduce(`+`) + plot_layout(ncol = 5, guides = "collect")lv <- 386
dmg <- getDMGsInLV(methPLIER, D, lv, ph$cluster.sample)dmg %>%
dim()
#> [1] 2775 6
dmg %>%
head()
#> # A tibble: 6 × 6
#> Gene id.gene `1` `2` p.value q.value
#> <chr> <int> <list> <list> <dbl> <dbl>
#> 1 AASDH 4026 <dbl [20]> <dbl [135]> 0.000243 0.000689
#> 2 ABCA5 3225 <dbl [20]> <dbl [135]> 0.000426 0.00115
#> 3 ABCA7 1679 <dbl [20]> <dbl [135]> 0.00000106 0.00000527
#> 4 ABCB8 1666 <dbl [20]> <dbl [135]> 0.00000000744 0.000000163
#> 5 ABCB9 4291 <dbl [20]> <dbl [135]> 0.000212 0.000609
#> 6 ABCC3 323 <dbl [20]> <dbl [135]> 0.00268 0.00583dmp <- getDMPs(gse39279.data, ph$cluster.sample, base::unique(dmg$Gene), threshold = 0.05,
method = "fdr")dmp %>%
head()
#> # A tibble: 6 × 10
#> TargetID `1` `2` p.value q.value genesUniq CpG_chrm CpG_beg CpG_end
#> <chr> <list> <lis> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 cg00000029 <dbl [20]> <dbl> 3.79e-4 1.10e-3 RBL2 chr16 5.34e7 5.34e7
#> 2 cg00000714 <dbl [20]> <dbl> 4.12e-4 1.18e-3 TSEN34 chr19 5.42e7 5.42e7
#> 3 cg00000924 <dbl [20]> <dbl> 5.79e-3 1.25e-2 KCNQ1 chr11 2.70e6 2.70e6
#> 4 cg00001582 <dbl [20]> <dbl> 1.36e-8 5.77e-7 ZMIZ1 chr10 7.91e7 7.91e7
#> 5 cg00003091 <dbl [20]> <dbl> 3.15e-5 1.23e-4 SLBP chr4 1.71e6 1.71e6
#> 6 cg00003091 <dbl [20]> <dbl> 3.15e-5 1.23e-4 TACC3 chr4 1.71e6 1.71e6
#> # … with 1 more variable: probe_strand <chr>pathway <- getPathway(dmg %>%
distinct(Gene) %>%
pull(Gene))pathway$barplotpathway$dotplotpathway$treeplot# Get gene list contained in the ontology 'Metastatic malignant neoplasm to
# brain'
gene.list <- pathway$edox2@result %>%
top_n(1, -(qvalue)) %>%
pull(geneID) %>%
str_split(., "\\/") %>%
unlist()
gz <- gene.list %>%
map(~.x %>%
makeGvizObj(., gse39279.data))
names(gz) <- gene.list# Plot ERBB2 as example
gz$ERBB2 %>%
plotGvizObj(.)
# If you want to plot all of the listed gene, execute below script.
# dir.create('figures/gviz_1/', recursive = TRUE) gz %>% imap(~ plotGvizObj(.x,
# filename = .y, dir = 'figures/gviz_1'))plotBoxplot.gviz(gz$ERBB2, as.factor(ph$cluster.sample$cluster.sample), gz$ERBB2$probe$TargetID[1:5])sessionInfo()
#> R version 4.1.1 (2021-08-10)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Catalina 10.15.7
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] patchwork_1.1.1 survival_3.3-1 methPLIER_0.1.0
#> [4] PLIER_0.99.0 qvalue_2.26.0 rsvd_1.0.5
#> [7] glmnet_4.1-4 Matrix_1.4-1 pheatmap_1.0.12
#> [10] gplots_3.1.3 RColorBrewer_1.1-3 BiocManager_1.30.18
#> [13] magrittr_2.0.3 forcats_0.5.1 stringr_1.4.0
#> [16] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
#> [19] tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
#> [22] tidyverse_1.3.1 rmdformats_1.0.4 knitr_1.39
#>
#> loaded via a namespace (and not attached):
#> [1] rappdirs_0.3.3 rtracklayer_1.54.0
#> [3] bit64_4.0.5 DelayedArray_0.20.0
#> [5] data.table_1.14.2 rpart_4.1.16
#> [7] AnnotationFilter_1.18.0 KEGGREST_1.34.0
#> [9] RCurl_1.98-1.6 doParallel_1.0.17
#> [11] generics_0.1.2 GenomicFeatures_1.46.5
#> [13] BiocGenerics_0.40.0 RSQLite_2.2.14
#> [15] shadowtext_0.1.2 bit_4.0.4
#> [17] tzdb_0.3.0 enrichplot_1.14.2
#> [19] xml2_1.3.3 lubridate_1.8.0
#> [21] SummarizedExperiment_1.24.0 assertthat_0.2.1
#> [23] viridis_0.6.2 xfun_0.31
#> [25] hms_1.1.1 jquerylib_0.1.4
#> [27] evaluate_0.15 fansi_1.0.3
#> [29] restfulr_0.0.13 progress_1.2.2
#> [31] caTools_1.18.2 dbplyr_2.1.1
#> [33] readxl_1.4.0 km.ci_0.5-6
#> [35] igraph_1.3.1 DBI_1.1.2
#> [37] htmlwidgets_1.5.4 stats4_4.1.1
#> [39] ellipsis_0.3.2 ggnewscale_0.4.7
#> [41] ggpubr_0.4.0 backports_1.4.1
#> [43] bookdown_0.26 biomaRt_2.50.3
#> [45] MatrixGenerics_1.6.0 vctrs_0.4.1
#> [47] Biobase_2.54.0 ensembldb_2.18.4
#> [49] abind_1.4-5 cachem_1.0.6
#> [51] withr_2.5.0 ggforce_0.3.3
#> [53] Gviz_1.38.4 BSgenome_1.62.0
#> [55] checkmate_2.1.0 GenomicAlignments_1.30.0
#> [57] treeio_1.18.1 prettyunits_1.1.1
#> [59] cluster_2.1.3 DOSE_3.20.1
#> [61] ape_5.6-2 lazyeval_0.2.2
#> [63] crayon_1.5.1 pkgconfig_2.0.3
#> [65] labeling_0.4.2 tweenr_1.0.2
#> [67] GenomeInfoDb_1.30.1 ProtGenerics_1.26.0
#> [69] nlme_3.1-157 nnet_7.3-17
#> [71] rlang_1.0.2 lifecycle_1.0.1
#> [73] filelock_1.0.2 BiocFileCache_2.2.1
#> [75] modelr_0.1.8 dichromat_2.0-0.1
#> [77] cellranger_1.1.0 polyclip_1.10-0
#> [79] matrixStats_0.62.0 aplot_0.1.4
#> [81] KMsurv_0.1-5 carData_3.0-5
#> [83] zoo_1.8-10 reprex_2.0.1
#> [85] base64enc_0.1-3 GlobalOptions_0.1.2
#> [87] png_0.1-7 viridisLite_0.4.0
#> [89] rjson_0.2.21 bitops_1.0-7
#> [91] KernSmooth_2.23-20 Biostrings_2.62.0
#> [93] blob_1.2.3 shape_1.4.6
#> [95] jpeg_0.1-9 rstatix_0.7.0
#> [97] gridGraphics_0.5-1 S4Vectors_0.32.4
#> [99] ggsignif_0.6.3 scales_1.2.0
#> [101] memoise_2.0.1 plyr_1.8.7
#> [103] zlibbioc_1.40.0 compiler_4.1.1
#> [105] scatterpie_0.1.7 BiocIO_1.4.0
#> [107] clue_0.3-60 Rsamtools_2.10.0
#> [109] cli_3.3.0 XVector_0.34.0
#> [111] htmlTable_2.4.0 formatR_1.12
#> [113] Formula_1.2-4 MASS_7.3-57
#> [115] tidyselect_1.1.2 stringi_1.7.6
#> [117] highr_0.9 yaml_2.3.5
#> [119] GOSemSim_2.20.0 latticeExtra_0.6-29
#> [121] ggrepel_0.9.1 survMisc_0.5.6
#> [123] grid_4.1.1 VariantAnnotation_1.40.0
#> [125] sass_0.4.1 fastmatch_1.1-3
#> [127] tools_4.1.1 parallel_4.1.1
#> [129] circlize_0.4.15 rstudioapi_0.13
#> [131] foreach_1.5.2 foreign_0.8-82
#> [133] gridExtra_2.3 farver_2.1.0
#> [135] ggraph_2.0.5 digest_0.6.29
#> [137] ggtext_0.1.1 Rcpp_1.0.8.3
#> [139] gridtext_0.1.4 GenomicRanges_1.46.1
#> [141] car_3.0-13 broom_0.8.0
#> [143] httr_1.4.3 survminer_0.4.9
#> [145] AnnotationDbi_1.56.2 biovizBase_1.42.0
#> [147] ComplexHeatmap_2.10.0 colorspace_2.0-3
#> [149] rvest_1.0.2 XML_3.99-0.9
#> [151] fs_1.5.2 IRanges_2.28.0
#> [153] splines_4.1.1 yulab.utils_0.0.4
#> [155] tidytree_0.3.9 graphlayouts_0.8.0
#> [157] ggplotify_0.1.0 xtable_1.8-4
#> [159] jsonlite_1.8.0 ggtree_3.2.1
#> [161] tidygraph_1.2.1 ggfun_0.0.6
#> [163] R6_2.5.1 Hmisc_4.7-0
#> [165] pillar_1.7.0 htmltools_0.5.2
#> [167] glue_1.6.2 fastmap_1.1.0
#> [169] BiocParallel_1.28.3 codetools_0.2-18
#> [171] fgsea_1.20.0 utf8_1.2.2
#> [173] lattice_0.20-45 bslib_0.3.1.9000
#> [175] curl_4.3.2 gtools_3.9.2.1
#> [177] GO.db_3.14.0 rmarkdown_2.14
#> [179] munsell_0.5.0 DO.db_2.9
#> [181] GetoptLong_1.0.5 GenomeInfoDbData_1.2.7
#> [183] iterators_1.0.14 haven_2.5.0
#> [185] reshape2_1.4.4 gtable_0.3.0